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Directory entires that have specified Iceland as one of the geographic regions for the project/activity and are included in the AMAP, ENVINET, SAON and SEARCH directories. Note that the list of regions is not hierarchical, and there is no relation between regions (e.g. a record tagged with Nunavut may not be tagged with Canada). To see the full list of regions, see the regions list. To browse the catalog based on the originating country (leady party), see the list of countries.
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RANNIS reports to the Ministry of Education, Science and Culture and operates according to the Act on Public Support for Scientific Research ( No. 3/2003). Hallgrímur Jónasson is the General Director of RANNIS.
The Icelandic Centre for Research (RANNIS) supports research, innovation, education and culture in Iceland. RANNIS cooperates closely with the Icelandic Science and Technology Policy Council and provides professional assistance in the preparation and implementation of the national science and technology policy.
RANNIS administers competitive funds in the fields of research, innovation, education and culture, as well as strategic research programmes.
RANNIS monitors resources and performance in R&D and promotes public awareness of research and innovation, education and culture in Iceland. Rannis is the Icelandic national contact point for SAON.
At the end of 2014, RANNIS had a permanent staff of 41. Apart from regular staff, RANNIS also relies on the involvement of external contacts, including scientists and technical experts who assist in the evaluation of grant proposals.
The main competitive funds administered by RANNIS have the following annual budgets for 2014: The Icelandic Research Fund: 1.185 MISK, The Infrastructure Fund: 106 MISK, The Technology Development Fund: 988 MISK.
A proposal has been submitted to the National Science Foundation titled: For Support of the Arctic Social Science Data Center at NSIDC, OPP-0119836.
The aim is to monitor the Lake Myvatn and the river Laxá ecosystem for (1) detecting trends, (2) detecting background variability in the system, (3) assess the efficiency of management measures, (4) observe perturbations in order to generate hypotheses about causal relationships.